Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Chattopadhyay, Soumitri"'
Although foundational vision-language models (VLMs) have proven to be very successful for various semantic discrimination tasks, they still struggle to perform faithfully for fine-grained categorization. Moreover, foundational models trained on one d
Externí odkaz:
http://arxiv.org/abs/2409.01835
The ability to retrieve a photo by mere free-hand sketching highlights the immense potential of Fine-grained sketch-based image retrieval (FG-SBIR). However, its rapid practical adoption, as well as scalability, is limited by the expense of acquiring
Externí odkaz:
http://arxiv.org/abs/2309.08743
In contemporary self-supervised contrastive algorithms like SimCLR, MoCo, etc., the task of balancing attraction between two semantically similar samples and repulsion between two samples of different classes is primarily affected by the presence of
Externí odkaz:
http://arxiv.org/abs/2308.01140
Autor:
Deb, Ahana, Nag, Sayan, Mahapatra, Ayan, Chattopadhyay, Soumitri, Marik, Aritra, Gayen, Pijush Kanti, Sanyal, Shankha, Banerjee, Archi, Karmakar, Samir
Spoken languages often utilise intonation, rhythm, intensity, and structure, to communicate intention, which can be interpreted differently depending on the rhythm of speech of their utterance. These speech acts provide the foundation of communicatio
Externí odkaz:
http://arxiv.org/abs/2306.02680
Autor:
Sain, Aneeshan, Bhunia, Ayan Kumar, Koley, Subhadeep, Chowdhury, Pinaki Nath, Chattopadhyay, Soumitri, Xiang, Tao, Song, Yi-Zhe
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%. This is not via complicated design though, but by addressing two critical iss
Externí odkaz:
http://arxiv.org/abs/2303.13779
Autor:
Chattopadhyay, Soumitri, Ganguly, Soham, Chaudhury, Sreejit, Nag, Sayan, Chattopadhyay, Samiran
Privacy and annotation bottlenecks are two major issues that profoundly affect the practicality of machine learning-based medical image analysis. Although significant progress has been made in these areas, these issues are not yet fully resolved. In
Externí odkaz:
http://arxiv.org/abs/2303.05556
Autor:
Chattopadhyay, Soumitri, Ganguly, Soham, Chaudhury, Sreejit, Nag, Sayan, Chattopadhyay, Samiran
The success of self-supervised learning (SSL) has mostly been attributed to the availability of unlabeled yet large-scale datasets. However, in a specialized domain such as medical imaging which is a lot different from natural images, the assumption
Externí odkaz:
http://arxiv.org/abs/2303.02245
Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level s
Externí odkaz:
http://arxiv.org/abs/2210.15075
Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data. To handle such data scarcity problem, in this paper, we propose a novel self-supervised learn
Externí odkaz:
http://arxiv.org/abs/2202.13078
Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate meta-heuristics that have successfu
Externí odkaz:
http://arxiv.org/abs/2201.12810